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Abstract
Despite the fact that there is an increasing amount of free and open Earth observation (EO)
data, additional information is not necessarily being generated at the same rate. Because
numerical, sensory data lack semantic meaning, the fundamental issue in the massive EO
analysis sector is producing information from EO data. Semantic data cube is an
advancement of the state-of-the-art EO data cube with each observation having a categorical
information linked to it which can be queried for analysis. Big EO data expert systems are
built on the foundation of semantic EO data cubes which can automatically deduce new
information from human-understandable semantic queries.
1. Introduction
A data cube can be broadly defined as a multidimensional array to organize data which is
typically organized in three-dimension, latitude, longitude, and space [1] and it simplifies
data storage, access and analysis compared to a file-based system. The concept of a data cube
has been quite prevalent over the past few years. For example, Australia was the first country
to establish a national scale EO data cube [2], whose technology is now the foundation of
Digital Earth Australia [3] and the Open Data Cube (ODC) [4]. Furthermore, ODC
technology is also behind other operational EO data cubes, such as in Columbia [5],
Switzerland [6], Vietnam [7], The African Regional Data Cube [8] and at least nine other
national or regional initiatives still in progress [4]. Developed in the mid-1990s, Rasdaman
[9], an array database system, is the leading technology behind initiatives such as EarthServer
[10] and the Copernicus Data and Exploitation platform for Germany (CODE-DE) [11].
These State-of-the-art EO data cubes simplify data provided to users by facilitating data
uptake and deliver analysis-ready data (ARD) [1]. ARD is the calibrated data; thus, it shifts
the burden of preprocessing the data from the users to the data providers, who are better
equipped to reliably process the Big Data [3].
Although, data cubes make data access effective and efficient by providing the users with
data personalized more specifically to their needs [12], however, the biggest challenge in EO
data analysis is to extract information from the vast volume of sensory, numerical data [1].
The EO data cubes lack semantics and therefore a semantic EO data cube is an advancement
of the state-of-the-art data cube facilitating production of knowledge from sensory data. “A
Semantic EO data cube can be defined as a spatio-temporal data cube containing EO data,